When viewing an event, for example, a sports game, a weather event, or a children's spelling bee, in person, human beings sometimes have emotional responses and visually or orally share their emotional responses with others. As a result, a viewer present at the event is able to determine the community emotional response to the event. For example, if the Red Sox hit a homerun during a game in Boston, a viewer will likely determine that the audience is joyful, happy, or excited based on the audio and visual reactions of the people at the event.
In some cases, users learn about events by reading articles in an online news source. Online news sources allow users to learn facts about events (e.g., the Red Sox hit a home run) quickly. However, in order to determine the community emotional response to the event, a user may need to read multiple comments on the article. The comments may be available in the online news source. Alternatively or additionally, the user may access a social networking service (e.g., Facebook® or Twitter®) to read multiple posts related to the article or the event. Determining the community emotional response to the article or the event based on the above methods may not be very efficient for a user who may wish to learn the community emotional response quickly and/or focus on articles to which the community has a certain emotional response. For example, a user who is trying to become happier or more optimistic may wish to read articles that have a happy or positive community emotional response and avoid articles with a sad or negative community emotional response. As the foregoing illustrates, a new approach for determining and presenting a community emotional response to an online content may be desirable.